Overview

Dataset statistics

Number of variables16
Number of observations506
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.4 KiB
Average record size in memory128.3 B

Variable types

Categorical2
Numeric14

Alerts

TOWN has a high cardinality: 92 distinct valuesHigh cardinality
TRACT is highly overall correlated with MEDV and 9 other fieldsHigh correlation
LON is highly overall correlated with TOWNHigh correlation
LAT is highly overall correlated with TOWNHigh correlation
MEDV is highly overall correlated with TRACT and 7 other fieldsHigh correlation
CRIM is highly overall correlated with TRACT and 8 other fieldsHigh correlation
ZN is highly overall correlated with CRIM and 5 other fieldsHigh correlation
INDUS is highly overall correlated with TRACT and 8 other fieldsHigh correlation
NOX is highly overall correlated with TRACT and 9 other fieldsHigh correlation
RM is highly overall correlated with MEDVHigh correlation
AGE is highly overall correlated with TRACT and 7 other fieldsHigh correlation
DIS is highly overall correlated with TRACT and 7 other fieldsHigh correlation
RAD is highly overall correlated with TRACT and 4 other fieldsHigh correlation
TAX is highly overall correlated with TRACT and 8 other fieldsHigh correlation
PTRATIO is highly overall correlated with TRACT and 2 other fieldsHigh correlation
TOWN is highly overall correlated with TRACT and 9 other fieldsHigh correlation
CHAS is highly imbalanced (63.7%)Imbalance
TRACT has unique valuesUnique
ZN has 372 (73.5%) zerosZeros

Reproduction

Analysis started2023-07-18 21:51:34.492263
Analysis finished2023-07-18 21:52:43.107930
Duration1 minute and 8.62 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

TOWN
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct92
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
Cambridge
 
30
Boston Savin Hill
 
23
Lynn
 
22
Boston Roxbury
 
19
Newton
 
18
Other values (87)
394 

Length

Max length23
Median length18
Mean length9.9743083
Min length4

Characters and Unicode

Total characters5047
Distinct characters41
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)3.4%

Sample

1st rowNahant
2nd rowSwampscott
3rd rowSwampscott
4th rowMarblehead
5th rowMarblehead

Common Values

ValueCountFrequency (%)
Cambridge 30
 
5.9%
Boston Savin Hill 23
 
4.5%
Lynn 22
 
4.3%
Boston Roxbury 19
 
3.8%
Newton 18
 
3.6%
Somerville 15
 
3.0%
Boston South Boston 13
 
2.6%
Quincy 12
 
2.4%
Brookline 12
 
2.4%
Boston East Boston 12
 
2.4%
Other values (82) 330
65.2%

Length

2023-07-18T21:52:43.325035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
boston 157
22.0%
cambridge 30
 
4.2%
hill 26
 
3.6%
savin 23
 
3.2%
roxbury 23
 
3.2%
lynn 22
 
3.1%
newton 18
 
2.5%
somerville 15
 
2.1%
south 13
 
1.8%
quincy 12
 
1.7%
Other values (87) 375
52.5%

Most occurring characters

ValueCountFrequency (%)
o 618
 
12.2%
n 465
 
9.2%
t 389
 
7.7%
e 378
 
7.5%
a 270
 
5.3%
r 264
 
5.2%
s 254
 
5.0%
l 250
 
5.0%
B 220
 
4.4%
i 219
 
4.3%
Other values (31) 1720
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4109
81.4%
Uppercase Letter 722
 
14.3%
Space Separator 208
 
4.1%
Dash Punctuation 8
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 618
15.0%
n 465
11.3%
t 389
9.5%
e 378
9.2%
a 270
 
6.6%
r 264
 
6.4%
s 254
 
6.2%
l 250
 
6.1%
i 219
 
5.3%
d 134
 
3.3%
Other values (13) 868
21.1%
Uppercase Letter
ValueCountFrequency (%)
B 220
30.5%
S 75
 
10.4%
W 65
 
9.0%
C 48
 
6.6%
H 44
 
6.1%
M 43
 
6.0%
R 42
 
5.8%
N 41
 
5.7%
L 31
 
4.3%
D 30
 
4.2%
Other values (6) 83
 
11.5%
Space Separator
ValueCountFrequency (%)
208
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4831
95.7%
Common 216
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 618
12.8%
n 465
 
9.6%
t 389
 
8.1%
e 378
 
7.8%
a 270
 
5.6%
r 264
 
5.5%
s 254
 
5.3%
l 250
 
5.2%
B 220
 
4.6%
i 219
 
4.5%
Other values (29) 1504
31.1%
Common
ValueCountFrequency (%)
208
96.3%
- 8
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5047
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 618
 
12.2%
n 465
 
9.2%
t 389
 
7.7%
e 378
 
7.5%
a 270
 
5.3%
r 264
 
5.2%
s 254
 
5.0%
l 250
 
5.0%
B 220
 
4.4%
i 219
 
4.3%
Other values (31) 1720
34.1%

TRACT
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct506
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2700.3557
Minimum1
Maximum5082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-07-18T21:52:43.617294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile430.5
Q11303.25
median3393.5
Q33739.75
95-th percentile4202.75
Maximum5082
Range5081
Interquartile range (IQR)2436.5

Descriptive statistics

Standard deviation1380.0368
Coefficient of variation (CV)0.51105742
Kurtosis-1.1960953
Mean2700.3557
Median Absolute Deviation (MAD)787
Skewness-0.43580814
Sum1366380
Variance1904501.7
MonotonicityNot monotonic
2023-07-18T21:52:43.945258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2011 1
 
0.2%
4212 1
 
0.2%
5021 1
 
0.2%
5012 1
 
0.2%
5011 1
 
0.2%
5001 1
 
0.2%
4231 1
 
0.2%
4228 1
 
0.2%
4227 1
 
0.2%
4226 1
 
0.2%
Other values (496) 496
98.0%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
101 1
0.2%
102 1
0.2%
ValueCountFrequency (%)
5082 1
0.2%
5081 1
0.2%
5071 1
0.2%
5062 1
0.2%
5061 1
0.2%
5052 1
0.2%
5051 1
0.2%
5041 1
0.2%
5031 1
0.2%
5022 1
0.2%

LON
Real number (ℝ)

Distinct375
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-71.056389
Minimum-71.2895
Maximum-70.81
Zeros0
Zeros (%)0.0%
Negative506
Negative (%)100.0%
Memory size4.1 KiB
2023-07-18T21:52:44.266569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-71.2895
5-th percentile-71.202375
Q1-71.093225
median-71.0529
Q3-71.019625
95-th percentile-70.936
Maximum-70.81
Range0.4795
Interquartile range (IQR)0.0736

Descriptive statistics

Standard deviation0.075405348
Coefficient of variation (CV)-0.0010612043
Kurtosis1.1084808
Mean-71.056389
Median Absolute Deviation (MAD)0.0371
Skewness-0.20538473
Sum-35954.533
Variance0.0056859665
MonotonicityNot monotonic
2023-07-18T21:52:44.602645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-71.069 5
 
1.0%
-71.03 4
 
0.8%
-71.02 4
 
0.8%
-71.0455 4
 
0.8%
-71.055 4
 
0.8%
-71.059 4
 
0.8%
-71.04 4
 
0.8%
-71.075 4
 
0.8%
-71.11 3
 
0.6%
-71.09 3
 
0.6%
Other values (365) 467
92.3%
ValueCountFrequency (%)
-71.2895 1
0.2%
-71.2807 1
0.2%
-71.269 1
0.2%
-71.2685 1
0.2%
-71.263 1
0.2%
-71.262 1
0.2%
-71.2575 1
0.2%
-71.255 1
0.2%
-71.2475 1
0.2%
-71.247 1
0.2%
ValueCountFrequency (%)
-70.81 1
0.2%
-70.83 2
0.4%
-70.833 1
0.2%
-70.8525 1
0.2%
-70.853 1
0.2%
-70.855 1
0.2%
-70.86 1
0.2%
-70.8875 1
0.2%
-70.9075 1
0.2%
-70.915 1
0.2%

LAT
Real number (ℝ)

Distinct376
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.21644
Minimum42.03
Maximum42.381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-07-18T21:52:44.943930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum42.03
5-th percentile42.10745
Q142.180775
median42.2181
Q342.25225
95-th percentile42.31985
Maximum42.381
Range0.351
Interquartile range (IQR)0.071475

Descriptive statistics

Standard deviation0.061777184
Coefficient of variation (CV)0.0014633442
Kurtosis0.10400249
Mean42.21644
Median Absolute Deviation (MAD)0.03625
Skewness-0.086678598
Sum21361.519
Variance0.0038164205
MonotonicityNot monotonic
2023-07-18T21:52:45.263118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.23 5
 
1.0%
42.192 4
 
0.8%
42.245 4
 
0.8%
42.188 4
 
0.8%
42.2075 4
 
0.8%
42.255 3
 
0.6%
42.225 3
 
0.6%
42.2875 3
 
0.6%
42.169 3
 
0.6%
42.305 3
 
0.6%
Other values (366) 470
92.9%
ValueCountFrequency (%)
42.03 1
0.2%
42.0485 1
0.2%
42.052 1
0.2%
42.059 2
0.4%
42.0675 1
0.2%
42.0725 2
0.4%
42.0735 1
0.2%
42.0775 2
0.4%
42.0795 1
0.2%
42.0825 1
0.2%
ValueCountFrequency (%)
42.381 1
0.2%
42.374 1
0.2%
42.3715 2
0.4%
42.3525 1
0.2%
42.346 2
0.4%
42.345 2
0.4%
42.3425 1
0.2%
42.34 1
0.2%
42.339 1
0.2%
42.3382 1
0.2%

MEDV
Real number (ℝ)

Distinct228
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.528854
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-07-18T21:52:45.629696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.2
Q117.025
median21.2
Q325
95-th percentile43.4
Maximum50
Range45
Interquartile range (IQR)7.975

Descriptive statistics

Standard deviation9.1821759
Coefficient of variation (CV)0.40757404
Kurtosis1.5167834
Mean22.528854
Median Absolute Deviation (MAD)4
Skewness1.1109119
Sum11399.6
Variance84.312354
MonotonicityNot monotonic
2023-07-18T21:52:45.937612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 16
 
3.2%
25 8
 
1.6%
23.1 7
 
1.4%
21.7 7
 
1.4%
19.4 6
 
1.2%
20.6 6
 
1.2%
22 6
 
1.2%
21.4 5
 
1.0%
21.2 5
 
1.0%
19.3 5
 
1.0%
Other values (218) 435
86.0%
ValueCountFrequency (%)
5 2
0.4%
5.6 1
 
0.2%
6.3 1
 
0.2%
7 2
0.4%
7.2 3
0.6%
7.4 1
 
0.2%
7.5 1
 
0.2%
8.1 1
 
0.2%
8.2 1
 
0.2%
8.3 2
0.4%
ValueCountFrequency (%)
50 16
3.2%
48.8 1
 
0.2%
48.5 1
 
0.2%
48.3 1
 
0.2%
46.7 1
 
0.2%
46 1
 
0.2%
45.4 1
 
0.2%
44.8 1
 
0.2%
44 1
 
0.2%
43.8 1
 
0.2%

CRIM
Real number (ℝ)

Distinct504
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6135236
Minimum0.00632
Maximum88.9762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-07-18T21:52:46.282235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.00632
5-th percentile0.02791
Q10.082045
median0.25651
Q33.6770825
95-th percentile15.78915
Maximum88.9762
Range88.96988
Interquartile range (IQR)3.5950375

Descriptive statistics

Standard deviation8.6015451
Coefficient of variation (CV)2.3803761
Kurtosis37.130509
Mean3.6135236
Median Absolute Deviation (MAD)0.22145
Skewness5.2231488
Sum1828.4429
Variance73.986578
MonotonicityNot monotonic
2023-07-18T21:52:46.588146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01501 2
 
0.4%
14.3337 2
 
0.4%
0.03466 1
 
0.2%
0.03113 1
 
0.2%
0.03049 1
 
0.2%
0.02543 1
 
0.2%
0.02498 1
 
0.2%
0.01301 1
 
0.2%
0.06151 1
 
0.2%
0.05497 1
 
0.2%
Other values (494) 494
97.6%
ValueCountFrequency (%)
0.00632 1
0.2%
0.00906 1
0.2%
0.01096 1
0.2%
0.01301 1
0.2%
0.01311 1
0.2%
0.0136 1
0.2%
0.01381 1
0.2%
0.01432 1
0.2%
0.01439 1
0.2%
0.01501 2
0.4%
ValueCountFrequency (%)
88.9762 1
0.2%
73.5341 1
0.2%
67.9208 1
0.2%
51.1358 1
0.2%
45.7461 1
0.2%
41.5292 1
0.2%
38.3518 1
0.2%
37.6619 1
0.2%
28.6558 1
0.2%
25.9406 1
0.2%

ZN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.363636
Minimum0
Maximum100
Zeros372
Zeros (%)73.5%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-07-18T21:52:46.878209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation23.322453
Coefficient of variation (CV)2.0523759
Kurtosis4.0315101
Mean11.363636
Median Absolute Deviation (MAD)0
Skewness2.2256663
Sum5750
Variance543.93681
MonotonicityNot monotonic
2023-07-18T21:52:47.160100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 372
73.5%
20 21
 
4.2%
80 15
 
3.0%
22 10
 
2.0%
12.5 10
 
2.0%
25 10
 
2.0%
40 7
 
1.4%
45 6
 
1.2%
30 6
 
1.2%
90 5
 
1.0%
Other values (16) 44
 
8.7%
ValueCountFrequency (%)
0 372
73.5%
12.5 10
 
2.0%
17.5 1
 
0.2%
18 1
 
0.2%
20 21
 
4.2%
21 4
 
0.8%
22 10
 
2.0%
25 10
 
2.0%
28 3
 
0.6%
30 6
 
1.2%
ValueCountFrequency (%)
100 1
 
0.2%
95 4
 
0.8%
90 5
 
1.0%
85 2
 
0.4%
82.5 2
 
0.4%
80 15
3.0%
75 3
 
0.6%
70 3
 
0.6%
60 4
 
0.8%
55 3
 
0.6%

INDUS
Real number (ℝ)

Distinct76
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.136779
Minimum0.46
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-07-18T21:52:47.443865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.18
Q15.19
median9.69
Q318.1
95-th percentile21.89
Maximum27.74
Range27.28
Interquartile range (IQR)12.91

Descriptive statistics

Standard deviation6.8603529
Coefficient of variation (CV)0.61600874
Kurtosis-1.2335396
Mean11.136779
Median Absolute Deviation (MAD)6.32
Skewness0.29502157
Sum5635.21
Variance47.064442
MonotonicityNot monotonic
2023-07-18T21:52:47.741138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1 132
26.1%
19.58 30
 
5.9%
8.14 22
 
4.3%
6.2 18
 
3.6%
21.89 15
 
3.0%
3.97 12
 
2.4%
9.9 12
 
2.4%
8.56 11
 
2.2%
10.59 11
 
2.2%
5.86 10
 
2.0%
Other values (66) 233
46.0%
ValueCountFrequency (%)
0.46 1
 
0.2%
0.74 1
 
0.2%
1.21 1
 
0.2%
1.22 1
 
0.2%
1.25 2
0.4%
1.32 1
 
0.2%
1.38 1
 
0.2%
1.47 2
0.4%
1.52 4
0.8%
1.69 2
0.4%
ValueCountFrequency (%)
27.74 5
 
1.0%
25.65 7
 
1.4%
21.89 15
 
3.0%
19.58 30
 
5.9%
18.1 132
26.1%
15.04 3
 
0.6%
13.92 5
 
1.0%
13.89 4
 
0.8%
12.83 6
 
1.2%
11.93 5
 
1.0%

CHAS
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
0
471 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters506
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 471
93.1%
1 35
 
6.9%

Length

2023-07-18T21:52:48.045847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-18T21:52:48.310278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 471
93.1%
1 35
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 471
93.1%
1 35
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 506
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 471
93.1%
1 35
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 506
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 471
93.1%
1 35
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 506
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 471
93.1%
1 35
 
6.9%

NOX
Real number (ℝ)

Distinct81
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55469506
Minimum0.385
Maximum0.871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-07-18T21:52:48.552608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.40925
Q10.449
median0.538
Q30.624
95-th percentile0.74
Maximum0.871
Range0.486
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.11587768
Coefficient of variation (CV)0.20890339
Kurtosis-0.064667133
Mean0.55469506
Median Absolute Deviation (MAD)0.0875
Skewness0.72930792
Sum280.6757
Variance0.013427636
MonotonicityNot monotonic
2023-07-18T21:52:48.863731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.538 23
 
4.5%
0.713 18
 
3.6%
0.437 17
 
3.4%
0.871 16
 
3.2%
0.624 15
 
3.0%
0.489 15
 
3.0%
0.693 14
 
2.8%
0.605 14
 
2.8%
0.74 13
 
2.6%
0.544 12
 
2.4%
Other values (71) 349
69.0%
ValueCountFrequency (%)
0.385 1
 
0.2%
0.389 1
 
0.2%
0.392 2
0.4%
0.394 1
 
0.2%
0.398 2
0.4%
0.4 4
0.8%
0.401 3
0.6%
0.403 3
0.6%
0.404 3
0.6%
0.405 3
0.6%
ValueCountFrequency (%)
0.871 16
3.2%
0.77 8
1.6%
0.74 13
2.6%
0.718 6
 
1.2%
0.713 18
3.6%
0.7 11
2.2%
0.693 14
2.8%
0.679 8
1.6%
0.671 7
 
1.4%
0.668 3
 
0.6%

RM
Real number (ℝ)

Distinct446
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2846344
Minimum3.561
Maximum8.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-07-18T21:52:49.188044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.314
Q15.8855
median6.2085
Q36.6235
95-th percentile7.5875
Maximum8.78
Range5.219
Interquartile range (IQR)0.738

Descriptive statistics

Standard deviation0.70261714
Coefficient of variation (CV)0.11179921
Kurtosis1.8915004
Mean6.2846344
Median Absolute Deviation (MAD)0.3455
Skewness0.40361213
Sum3180.025
Variance0.49367085
MonotonicityNot monotonic
2023-07-18T21:52:49.521768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.713 3
 
0.6%
6.167 3
 
0.6%
6.127 3
 
0.6%
6.229 3
 
0.6%
6.405 3
 
0.6%
6.417 3
 
0.6%
6.782 2
 
0.4%
6.951 2
 
0.4%
6.63 2
 
0.4%
6.312 2
 
0.4%
Other values (436) 480
94.9%
ValueCountFrequency (%)
3.561 1
0.2%
3.863 1
0.2%
4.138 2
0.4%
4.368 1
0.2%
4.519 1
0.2%
4.628 1
0.2%
4.652 1
0.2%
4.88 1
0.2%
4.903 1
0.2%
4.906 1
0.2%
ValueCountFrequency (%)
8.78 1
0.2%
8.725 1
0.2%
8.704 1
0.2%
8.398 1
0.2%
8.375 1
0.2%
8.337 1
0.2%
8.297 1
0.2%
8.266 1
0.2%
8.259 1
0.2%
8.247 1
0.2%

AGE
Real number (ℝ)

Distinct356
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.574901
Minimum2.9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-07-18T21:52:49.821090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile17.725
Q145.025
median77.5
Q394.075
95-th percentile100
Maximum100
Range97.1
Interquartile range (IQR)49.05

Descriptive statistics

Standard deviation28.148861
Coefficient of variation (CV)0.41048344
Kurtosis-0.96771559
Mean68.574901
Median Absolute Deviation (MAD)19.55
Skewness-0.59896264
Sum34698.9
Variance792.3584
MonotonicityNot monotonic
2023-07-18T21:52:50.268181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 43
 
8.5%
95.4 4
 
0.8%
96 4
 
0.8%
98.2 4
 
0.8%
97.9 4
 
0.8%
98.8 4
 
0.8%
87.9 4
 
0.8%
95.6 3
 
0.6%
97 3
 
0.6%
21.4 3
 
0.6%
Other values (346) 430
85.0%
ValueCountFrequency (%)
2.9 1
0.2%
6 1
0.2%
6.2 1
0.2%
6.5 1
0.2%
6.6 2
0.4%
6.8 1
0.2%
7.8 2
0.4%
8.4 1
0.2%
8.9 1
0.2%
9.8 1
0.2%
ValueCountFrequency (%)
100 43
8.5%
99.3 1
 
0.2%
99.1 1
 
0.2%
98.9 3
 
0.6%
98.8 4
 
0.8%
98.7 1
 
0.2%
98.5 1
 
0.2%
98.4 2
 
0.4%
98.3 2
 
0.4%
98.2 4
 
0.8%

DIS
Real number (ℝ)

Distinct412
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7950427
Minimum1.1296
Maximum12.1265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-07-18T21:52:50.783662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.461975
Q12.100175
median3.20745
Q35.188425
95-th percentile7.8278
Maximum12.1265
Range10.9969
Interquartile range (IQR)3.08825

Descriptive statistics

Standard deviation2.1057101
Coefficient of variation (CV)0.55485809
Kurtosis0.48794112
Mean3.7950427
Median Absolute Deviation (MAD)1.29115
Skewness1.0117806
Sum1920.2916
Variance4.4340151
MonotonicityNot monotonic
2023-07-18T21:52:51.256278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.4952 5
 
1.0%
5.7209 4
 
0.8%
5.2873 4
 
0.8%
6.8147 4
 
0.8%
5.4007 4
 
0.8%
6.3361 3
 
0.6%
3.9454 3
 
0.6%
6.498 3
 
0.6%
4.7211 3
 
0.6%
4.8122 3
 
0.6%
Other values (402) 470
92.9%
ValueCountFrequency (%)
1.1296 1
0.2%
1.137 1
0.2%
1.1691 1
0.2%
1.1742 1
0.2%
1.1781 1
0.2%
1.2024 1
0.2%
1.2852 1
0.2%
1.3163 1
0.2%
1.3216 1
0.2%
1.3325 1
0.2%
ValueCountFrequency (%)
12.1265 1
0.2%
10.7103 2
0.4%
10.5857 2
0.4%
9.2229 1
0.2%
9.2203 2
0.4%
9.1876 1
0.2%
9.0892 1
0.2%
8.9067 2
0.4%
8.7921 2
0.4%
8.6966 1
0.2%

RAD
Real number (ℝ)

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5494071
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-07-18T21:52:51.755187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.7072594
Coefficient of variation (CV)0.91181152
Kurtosis-0.86723199
Mean9.5494071
Median Absolute Deviation (MAD)2
Skewness1.0048146
Sum4832
Variance75.816366
MonotonicityNot monotonic
2023-07-18T21:52:52.299325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24 132
26.1%
5 115
22.7%
4 110
21.7%
3 38
 
7.5%
6 26
 
5.1%
2 24
 
4.7%
8 24
 
4.7%
1 20
 
4.0%
7 17
 
3.4%
ValueCountFrequency (%)
1 20
 
4.0%
2 24
 
4.7%
3 38
 
7.5%
4 110
21.7%
5 115
22.7%
6 26
 
5.1%
7 17
 
3.4%
8 24
 
4.7%
24 132
26.1%
ValueCountFrequency (%)
24 132
26.1%
8 24
 
4.7%
7 17
 
3.4%
6 26
 
5.1%
5 115
22.7%
4 110
21.7%
3 38
 
7.5%
2 24
 
4.7%
1 20
 
4.0%

TAX
Real number (ℝ)

Distinct66
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.23715
Minimum187
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-07-18T21:52:52.797518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile222
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range524
Interquartile range (IQR)387

Descriptive statistics

Standard deviation168.53712
Coefficient of variation (CV)0.4128412
Kurtosis-1.142408
Mean408.23715
Median Absolute Deviation (MAD)73
Skewness0.66995594
Sum206568
Variance28404.759
MonotonicityNot monotonic
2023-07-18T21:52:53.355912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666 132
26.1%
307 40
 
7.9%
403 30
 
5.9%
437 15
 
3.0%
304 14
 
2.8%
264 12
 
2.4%
398 12
 
2.4%
384 11
 
2.2%
277 11
 
2.2%
224 10
 
2.0%
Other values (56) 219
43.3%
ValueCountFrequency (%)
187 1
 
0.2%
188 7
1.4%
193 8
1.6%
198 1
 
0.2%
216 5
1.0%
222 7
1.4%
223 5
1.0%
224 10
2.0%
226 1
 
0.2%
233 9
1.8%
ValueCountFrequency (%)
711 5
 
1.0%
666 132
26.1%
469 1
 
0.2%
437 15
 
3.0%
432 9
 
1.8%
430 3
 
0.6%
422 1
 
0.2%
411 2
 
0.4%
403 30
 
5.9%
402 2
 
0.4%

PTRATIO
Real number (ℝ)

Distinct46
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.455534
Minimum12.6
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-07-18T21:52:53.904801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.4
median19.05
Q320.2
95-th percentile21
Maximum22
Range9.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1649455
Coefficient of variation (CV)0.11730604
Kurtosis-0.28509138
Mean18.455534
Median Absolute Deviation (MAD)1.15
Skewness-0.80232493
Sum9338.5
Variance4.6869891
MonotonicityNot monotonic
2023-07-18T21:52:54.383367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
20.2 140
27.7%
14.7 34
 
6.7%
21 27
 
5.3%
17.8 23
 
4.5%
19.2 19
 
3.8%
17.4 18
 
3.6%
18.6 17
 
3.4%
19.1 17
 
3.4%
18.4 16
 
3.2%
16.6 16
 
3.2%
Other values (36) 179
35.4%
ValueCountFrequency (%)
12.6 3
 
0.6%
13 12
 
2.4%
13.6 1
 
0.2%
14.4 1
 
0.2%
14.7 34
6.7%
14.8 3
 
0.6%
14.9 4
 
0.8%
15.1 1
 
0.2%
15.2 13
 
2.6%
15.3 3
 
0.6%
ValueCountFrequency (%)
22 2
 
0.4%
21.2 15
 
3.0%
21.1 1
 
0.2%
21 27
 
5.3%
20.9 11
 
2.2%
20.2 140
27.7%
20.1 5
 
1.0%
19.7 8
 
1.6%
19.6 8
 
1.6%
19.2 19
 
3.8%

Interactions

2023-07-18T21:52:36.439691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:42.162573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:47.261351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:51.247884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:56.629531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:00.443116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:04.204994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:08.667409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:13.155479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:16.474522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:20.059686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:25.464320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:29.071005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:32.734640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:36.889916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:42.629380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:47.798279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:51.521054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:56.916537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:00.695446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:04.451482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:09.094490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:13.374542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:16.720838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:20.319912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:25.718988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:29.342096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:32.998104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:37.297468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:42.977429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:48.065820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:51.809398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:57.200136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:00.939682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:04.693559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:09.536977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:13.620524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:16.949575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:20.599473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:25.958073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:29.615658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:33.246533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:37.648258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:43.379029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:48.324808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:52.252582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:57.448277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:01.186215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:04.944781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:10.352776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:13.851844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:17.204275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:20.877923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:26.223026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:29.878015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:33.511683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:37.969661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:43.787688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:48.560474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:52.655818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:51:57.960750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:01.467367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:05.204325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-18T21:52:10.741402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/